Hybrid quantum convolutional neural network-aided pilot assignment in cell-free massive MIMO systems
Hybrid quantum convolutional neural network-aided pilot assignment in cell-free massive MIMO systems
A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.
Cell-free massive MIMO, Pilot Allocation, Quantum Machine Learning
Nguyen, Doan Hieu
1f403753-50cd-48fe-9d19-84363a6533f4
Nguyen, Xuan Tung
e2dfcebd-7655-43a7-a77f-1328f8b2d4f7
Jeong, Seon-Geun
acc0ad8e-5c85-4592-b53b-21b8f87044b0
Chien, Trinh Van
cffa436d-56fe-42a8-b1c8-776045e330b0
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Hwang, Won-Joo
54cf7613-ee3a-49c0-b19f-67442fe2a4ac
23 July 2025
Nguyen, Doan Hieu
1f403753-50cd-48fe-9d19-84363a6533f4
Nguyen, Xuan Tung
e2dfcebd-7655-43a7-a77f-1328f8b2d4f7
Jeong, Seon-Geun
acc0ad8e-5c85-4592-b53b-21b8f87044b0
Chien, Trinh Van
cffa436d-56fe-42a8-b1c8-776045e330b0
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Hwang, Won-Joo
54cf7613-ee3a-49c0-b19f-67442fe2a4ac
Nguyen, Doan Hieu, Nguyen, Xuan Tung, Jeong, Seon-Geun, Chien, Trinh Van, Hanzo, Lajos and Hwang, Won-Joo
(2025)
Hybrid quantum convolutional neural network-aided pilot assignment in cell-free massive MIMO systems.
IEEE Transactions on Vehicular Technology.
(doi:10.1109/TVT.2025.3588212).
Abstract
A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.
Text
VT_2025_00147
- Accepted Manuscript
More information
Accepted/In Press date: 9 July 2025
e-pub ahead of print date: 23 July 2025
Published date: 23 July 2025
Keywords:
Cell-free massive MIMO, Pilot Allocation, Quantum Machine Learning
Identifiers
Local EPrints ID: 504068
URI: http://eprints.soton.ac.uk/id/eprint/504068
ISSN: 0018-9545
PURE UUID: 7ee8b61a-eba2-4436-abdd-75cd88a4e50b
Catalogue record
Date deposited: 22 Aug 2025 16:36
Last modified: 18 Oct 2025 01:33
Export record
Altmetrics
Contributors
Author:
Doan Hieu Nguyen
Author:
Xuan Tung Nguyen
Author:
Seon-Geun Jeong
Author:
Trinh Van Chien
Author:
Lajos Hanzo
Author:
Won-Joo Hwang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics